Overview

Dataset statistics

Number of variables24
Number of observations3720
Missing cells6802
Missing cells (%)7.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.4 MiB
Average record size in memory675.3 B

Variable types

Categorical13
Numeric11

Alerts

society has a high cardinality: 682 distinct valuesHigh cardinality
sector has a high cardinality: 107 distinct valuesHigh cardinality
areaWithType has a high cardinality: 2375 distinct valuesHigh cardinality
price_cr is highly overall correlated with price_per_sqft and 7 other fieldsHigh correlation
price_per_sqft is highly overall correlated with price_crHigh correlation
area is highly overall correlated with price_cr and 5 other fieldsHigh correlation
bedRoom is highly overall correlated with price_cr and 5 other fieldsHigh correlation
bathroom is highly overall correlated with price_cr and 4 other fieldsHigh correlation
super_built_up_area is highly overall correlated with price_cr and 7 other fieldsHigh correlation
built_up_area is highly overall correlated with price_cr and 4 other fieldsHigh correlation
carpet_area is highly overall correlated with price_cr and 5 other fieldsHigh correlation
property_type is highly overall correlated with price_cr and 2 other fieldsHigh correlation
facing is highly overall correlated with built_up_areaHigh correlation
servant room is highly overall correlated with super_built_up_areaHigh correlation
store room is highly imbalanced (55.5%)Imbalance
facing has 1061 (28.5%) missing valuesMissing
super_built_up_area has 1844 (49.6%) missing valuesMissing
built_up_area has 1995 (53.6%) missing valuesMissing
carpet_area has 1833 (49.3%) missing valuesMissing
area is highly skewed (γ1 = 29.89518153)Skewed
built_up_area is highly skewed (γ1 = 41.04521156)Skewed
carpet_area is highly skewed (γ1 = 24.43032572)Skewed
floorNum has 130 (3.5%) zerosZeros
luxury_score has 476 (12.8%) zerosZeros
nearbyLoc_score has 199 (5.3%) zerosZeros

Reproduction

Analysis started2023-09-22 06:40:36.633717
Analysis finished2023-09-22 06:41:02.981365
Duration26.35 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

property_type
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size251.5 KiB
flat
2833 
house
887 

Length

Max length5
Median length4
Mean length4.2384409
Min length4

Characters and Unicode

Total characters15767
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowflat
2nd rowflat
3rd rowflat
4th rowflat
5th rowflat

Common Values

ValueCountFrequency (%)
flat 2833
76.2%
house 887
 
23.8%

Length

2023-09-22T12:11:03.120933image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-22T12:11:03.376516image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
flat 2833
76.2%
house 887
 
23.8%

Most occurring characters

ValueCountFrequency (%)
f 2833
18.0%
l 2833
18.0%
a 2833
18.0%
t 2833
18.0%
h 887
 
5.6%
o 887
 
5.6%
u 887
 
5.6%
s 887
 
5.6%
e 887
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 15767
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
f 2833
18.0%
l 2833
18.0%
a 2833
18.0%
t 2833
18.0%
h 887
 
5.6%
o 887
 
5.6%
u 887
 
5.6%
s 887
 
5.6%
e 887
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 15767
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
f 2833
18.0%
l 2833
18.0%
a 2833
18.0%
t 2833
18.0%
h 887
 
5.6%
o 887
 
5.6%
u 887
 
5.6%
s 887
 
5.6%
e 887
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15767
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
f 2833
18.0%
l 2833
18.0%
a 2833
18.0%
t 2833
18.0%
h 887
 
5.6%
o 887
 
5.6%
u 887
 
5.6%
s 887
 
5.6%
e 887
 
5.6%

society
Categorical

Distinct682
Distinct (%)18.3%
Missing1
Missing (%)< 0.1%
Memory size297.3 KiB
independent
512 
tulip violet
 
75
ss the leaf
 
73
shapoorji pallonji joyville gurugram
 
42
dlf new town heights
 
42
Other values (677)
2975 

Length

Max length49
Median length40
Mean length16.840549
Min length1

Characters and Unicode

Total characters62630
Distinct characters41
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique310 ?
Unique (%)8.3%

Sample

1st rowexperion the heartsong
2nd rowtulip violet
3rd rowshree vardhman victoria
4th rowsare green parc phase 3
5th rowmvn athens

Common Values

ValueCountFrequency (%)
independent 512
 
13.8%
tulip violet 75
 
2.0%
ss the leaf 73
 
2.0%
shapoorji pallonji joyville gurugram 42
 
1.1%
dlf new town heights 42
 
1.1%
signature global park 35
 
0.9%
shree vardhman victoria 34
 
0.9%
smart world orchard 33
 
0.9%
emaar mgf emerald floors premier 32
 
0.9%
dlf the ultima 31
 
0.8%
Other values (672) 2810
75.5%

Length

2023-09-22T12:11:03.533712image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
independent 517
 
5.3%
the 349
 
3.6%
dlf 220
 
2.3%
park 211
 
2.2%
city 166
 
1.7%
emaar 155
 
1.6%
m3m 152
 
1.6%
global 152
 
1.6%
signature 149
 
1.5%
heights 134
 
1.4%
Other values (785) 7545
77.4%

Most occurring characters

ValueCountFrequency (%)
e 6821
 
10.9%
6033
 
9.6%
a 5898
 
9.4%
n 4260
 
6.8%
r 4203
 
6.7%
i 3870
 
6.2%
t 3762
 
6.0%
s 3496
 
5.6%
l 2955
 
4.7%
o 2768
 
4.4%
Other values (31) 18564
29.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 56045
89.5%
Space Separator 6033
 
9.6%
Decimal Number 531
 
0.8%
Other Punctuation 12
 
< 0.1%
Dash Punctuation 9
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 6821
12.2%
a 5898
 
10.5%
n 4260
 
7.6%
r 4203
 
7.5%
i 3870
 
6.9%
t 3762
 
6.7%
s 3496
 
6.2%
l 2955
 
5.3%
o 2768
 
4.9%
d 2547
 
4.5%
Other values (16) 15465
27.6%
Decimal Number
ValueCountFrequency (%)
3 207
39.0%
2 81
 
15.3%
1 77
 
14.5%
6 56
 
10.5%
8 32
 
6.0%
4 19
 
3.6%
5 17
 
3.2%
7 15
 
2.8%
9 14
 
2.6%
0 13
 
2.4%
Other Punctuation
ValueCountFrequency (%)
, 9
75.0%
/ 2
 
16.7%
. 1
 
8.3%
Space Separator
ValueCountFrequency (%)
6033
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 56045
89.5%
Common 6585
 
10.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 6821
12.2%
a 5898
 
10.5%
n 4260
 
7.6%
r 4203
 
7.5%
i 3870
 
6.9%
t 3762
 
6.7%
s 3496
 
6.2%
l 2955
 
5.3%
o 2768
 
4.9%
d 2547
 
4.5%
Other values (16) 15465
27.6%
Common
ValueCountFrequency (%)
6033
91.6%
3 207
 
3.1%
2 81
 
1.2%
1 77
 
1.2%
6 56
 
0.9%
8 32
 
0.5%
4 19
 
0.3%
5 17
 
0.3%
7 15
 
0.2%
9 14
 
0.2%
Other values (5) 34
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 62630
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 6821
 
10.9%
6033
 
9.6%
a 5898
 
9.4%
n 4260
 
6.8%
r 4203
 
6.7%
i 3870
 
6.2%
t 3762
 
6.0%
s 3496
 
5.6%
l 2955
 
4.7%
o 2768
 
4.4%
Other values (31) 18564
29.6%

sector
Categorical

Distinct107
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size269.6 KiB
sohna road
 
168
sector 85
 
108
sector 102
 
107
sector 92
 
100
sector 69
 
93
Other values (102)
3144 

Length

Max length10
Median length9
Mean length9.2067204
Min length8

Characters and Unicode

Total characters34249
Distinct characters21
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowsector 108
2nd rowsector 69
3rd rowsector 70
4th rowsector 92
5th rowsohna road

Common Values

ValueCountFrequency (%)
sohna road 168
 
4.5%
sector 85 108
 
2.9%
sector 102 107
 
2.9%
sector 92 100
 
2.7%
sector 69 93
 
2.5%
sector 90 89
 
2.4%
sector 65 87
 
2.3%
sector 81 87
 
2.3%
sector 109 86
 
2.3%
sector 79 77
 
2.1%
Other values (97) 2718
73.1%

Length

2023-09-22T12:11:03.788023image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sector 3552
47.7%
sohna 168
 
2.3%
road 168
 
2.3%
85 108
 
1.5%
102 107
 
1.4%
92 100
 
1.3%
69 93
 
1.2%
90 89
 
1.2%
65 87
 
1.2%
81 87
 
1.2%
Other values (99) 2881
38.7%

Most occurring characters

ValueCountFrequency (%)
o 3888
11.4%
s 3720
10.9%
r 3720
10.9%
3720
10.9%
c 3603
10.5%
e 3552
10.4%
t 3552
10.4%
1 1125
 
3.3%
0 808
 
2.4%
8 782
 
2.3%
Other values (11) 5779
16.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 23128
67.5%
Decimal Number 7401
 
21.6%
Space Separator 3720
 
10.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 3888
16.8%
s 3720
16.1%
r 3720
16.1%
c 3603
15.6%
e 3552
15.4%
t 3552
15.4%
a 526
 
2.3%
d 231
 
1.0%
n 168
 
0.7%
h 168
 
0.7%
Decimal Number
ValueCountFrequency (%)
1 1125
15.2%
0 808
10.9%
8 782
10.6%
9 765
10.3%
6 749
10.1%
7 691
9.3%
2 688
9.3%
3 669
9.0%
5 621
8.4%
4 503
6.8%
Space Separator
ValueCountFrequency (%)
3720
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 23128
67.5%
Common 11121
32.5%

Most frequent character per script

Common
ValueCountFrequency (%)
3720
33.5%
1 1125
 
10.1%
0 808
 
7.3%
8 782
 
7.0%
9 765
 
6.9%
6 749
 
6.7%
7 691
 
6.2%
2 688
 
6.2%
3 669
 
6.0%
5 621
 
5.6%
Latin
ValueCountFrequency (%)
o 3888
16.8%
s 3720
16.1%
r 3720
16.1%
c 3603
15.6%
e 3552
15.4%
t 3552
15.4%
a 526
 
2.3%
d 231
 
1.0%
n 168
 
0.7%
h 168
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 34249
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 3888
11.4%
s 3720
10.9%
r 3720
10.9%
3720
10.9%
c 3603
10.5%
e 3552
10.4%
t 3552
10.4%
1 1125
 
3.3%
0 808
 
2.4%
8 782
 
2.3%
Other values (11) 5779
16.9%

price_cr
Real number (ℝ)

Distinct474
Distinct (%)12.8%
Missing16
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean2.5302997
Minimum0.07
Maximum31.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size58.1 KiB
2023-09-22T12:11:03.974731image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0.07
5-th percentile0.37
Q10.95
median1.52
Q32.75
95-th percentile8.5
Maximum31.5
Range31.43
Interquartile range (IQR)1.8

Descriptive statistics

Standard deviation2.9730588
Coefficient of variation (CV)1.1749829
Kurtosis14.932686
Mean2.5302997
Median Absolute Deviation (MAD)0.73
Skewness3.2746171
Sum9372.23
Variance8.8390784
MonotonicityNot monotonic
2023-09-22T12:11:04.178320image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.25 80
 
2.2%
1.5 65
 
1.7%
1.2 64
 
1.7%
0.9 64
 
1.7%
1.1 62
 
1.7%
1.4 61
 
1.6%
1.3 58
 
1.6%
0.95 55
 
1.5%
2 53
 
1.4%
1.75 48
 
1.3%
Other values (464) 3094
83.2%
ValueCountFrequency (%)
0.07 1
 
< 0.1%
0.16 1
 
< 0.1%
0.17 1
 
< 0.1%
0.19 1
 
< 0.1%
0.2 8
0.2%
0.21 6
0.2%
0.22 9
0.2%
0.23 2
 
0.1%
0.24 6
0.2%
0.25 11
0.3%
ValueCountFrequency (%)
31.5 1
 
< 0.1%
27.5 1
 
< 0.1%
26 2
0.1%
25 1
 
< 0.1%
24 1
 
< 0.1%
23 1
 
< 0.1%
22 1
 
< 0.1%
20 3
0.1%
19.5 2
0.1%
19 3
0.1%

price_per_sqft
Real number (ℝ)

Distinct2671
Distinct (%)72.1%
Missing16
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean13954.85
Minimum4
Maximum600000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size58.1 KiB
2023-09-22T12:11:04.334358image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile4705.15
Q16812.75
median9023
Q313889
95-th percentile33333
Maximum600000
Range599996
Interquartile range (IQR)7076.25

Descriptive statistics

Standard deviation23300.036
Coefficient of variation (CV)1.669673
Kurtosis182.12387
Mean13954.85
Median Absolute Deviation (MAD)2805
Skewness11.253883
Sum51688765
Variance5.428917 × 108
MonotonicityNot monotonic
2023-09-22T12:11:04.669946image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10000 27
 
0.7%
8000 20
 
0.5%
5000 18
 
0.5%
22222 14
 
0.4%
11111 14
 
0.4%
12500 14
 
0.4%
6666 13
 
0.3%
7500 12
 
0.3%
8333 12
 
0.3%
6000 11
 
0.3%
Other values (2661) 3549
95.4%
(Missing) 16
 
0.4%
ValueCountFrequency (%)
4 1
< 0.1%
5 1
< 0.1%
7 1
< 0.1%
9 1
< 0.1%
53 1
< 0.1%
57 1
< 0.1%
58 2
0.1%
60 1
< 0.1%
61 1
< 0.1%
79 1
< 0.1%
ValueCountFrequency (%)
600000 1
< 0.1%
400000 1
< 0.1%
315789 1
< 0.1%
308333 1
< 0.1%
290948 1
< 0.1%
283333 1
< 0.1%
266666 1
< 0.1%
261194 1
< 0.1%
245398 1
< 0.1%
241666 1
< 0.1%

area
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct1325
Distinct (%)35.8%
Missing16
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean2879.2171
Minimum45
Maximum875000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size58.1 KiB
2023-09-22T12:11:04.834159image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum45
5-th percentile510.6
Q11223
median1728
Q32300
95-th percentile4249.4
Maximum875000
Range874955
Interquartile range (IQR)1077

Descriptive statistics

Standard deviation23033.372
Coefficient of variation (CV)7.9998734
Kurtosis952.77155
Mean2879.2171
Median Absolute Deviation (MAD)530
Skewness29.895182
Sum10664620
Variance5.3053623 × 108
MonotonicityNot monotonic
2023-09-22T12:11:04.993494image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1650 54
 
1.5%
1350 49
 
1.3%
1800 47
 
1.3%
3240 45
 
1.2%
1950 43
 
1.2%
900 42
 
1.1%
2700 39
 
1.0%
2000 33
 
0.9%
2250 25
 
0.7%
2400 23
 
0.6%
Other values (1315) 3304
88.8%
ValueCountFrequency (%)
45 1
 
< 0.1%
50 4
0.1%
55 1
 
< 0.1%
56 1
 
< 0.1%
57 1
 
< 0.1%
60 2
0.1%
61 1
 
< 0.1%
67 2
0.1%
70 1
 
< 0.1%
72 1
 
< 0.1%
ValueCountFrequency (%)
875000 1
< 0.1%
642857 1
< 0.1%
620000 1
< 0.1%
566667 1
< 0.1%
215517 1
< 0.1%
98978 1
< 0.1%
82781 1
< 0.1%
65517 2
0.1%
65261 1
< 0.1%
58228 1
< 0.1%

areaWithType
Categorical

Distinct2375
Distinct (%)63.8%
Missing0
Missing (%)0.0%
Memory size432.5 KiB
Plot area 360(301.01 sq.m.)
 
38
Plot area 300(250.84 sq.m.)
 
26
Plot area 200(167.23 sq.m.)
 
19
Plot area 502(419.74 sq.m.)
 
19
Super Built up area 1950(181.16 sq.m.)Carpet area: 1161 sq.ft. (107.86 sq.m.)
 
17
Other values (2370)
3601 

Length

Max length124
Median length119
Mean length54.050806
Min length12

Characters and Unicode

Total characters201069
Distinct characters35
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1866 ?
Unique (%)50.2%

Sample

1st rowSuper Built up area 2779(258.18 sq.m.)Built Up area: 2204.25 sq.ft. (204.78 sq.m.)Carpet area: 1631.07 sq.ft. (151.53 sq.m.)
2nd rowSuper Built up area 1578(146.6 sq.m.)Carpet area: 1538 sq.ft. (142.88 sq.m.)
3rd rowSuper Built up area 1950(181.16 sq.m.)Carpet area: 1161 sq.ft. (107.86 sq.m.)
4th rowSuper Built up area 1956(181.72 sq.m.)
5th rowBuilt Up area: 481 (44.69 sq.m.)

Common Values

ValueCountFrequency (%)
Plot area 360(301.01 sq.m.) 38
 
1.0%
Plot area 300(250.84 sq.m.) 26
 
0.7%
Plot area 200(167.23 sq.m.) 19
 
0.5%
Plot area 502(419.74 sq.m.) 19
 
0.5%
Super Built up area 1950(181.16 sq.m.)Carpet area: 1161 sq.ft. (107.86 sq.m.) 17
 
0.5%
Plot area 270(225.75 sq.m.) 17
 
0.5%
Super Built up area 1578(146.6 sq.m.) 17
 
0.5%
Plot area 900(83.61 sq.m.) 16
 
0.4%
Super Built up area 1350(125.42 sq.m.) 15
 
0.4%
Plot area 150(125.42 sq.m.) 14
 
0.4%
Other values (2365) 3522
94.7%

Length

2023-09-22T12:11:05.265448image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
area 5626
18.5%
sq.m 3698
12.2%
up 3034
 
10.0%
built 2328
 
7.7%
super 1876
 
6.2%
sq.ft 1757
 
5.8%
sq.m.)carpet 1193
 
3.9%
plot 705
 
2.3%
sq.m.)built 704
 
2.3%
carpet 690
 
2.3%
Other values (2865) 8785
28.9%

Most occurring characters

ValueCountFrequency (%)
26676
 
13.3%
. 20565
 
10.2%
a 13279
 
6.6%
r 9529
 
4.7%
e 9389
 
4.7%
1 9273
 
4.6%
s 7634
 
3.8%
q 7494
 
3.7%
t 7383
 
3.7%
p 6797
 
3.4%
Other values (25) 83050
41.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 83403
41.5%
Decimal Number 47544
23.6%
Space Separator 26676
 
13.3%
Other Punctuation 23610
 
11.7%
Uppercase Letter 8660
 
4.3%
Close Punctuation 5588
 
2.8%
Open Punctuation 5588
 
2.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 13279
15.9%
r 9529
11.4%
e 9389
11.3%
s 7634
9.2%
q 7494
9.0%
t 7383
8.9%
p 6797
8.1%
u 6786
8.1%
m 5597
6.7%
l 3739
 
4.5%
Other values (5) 5776
6.9%
Decimal Number
ValueCountFrequency (%)
1 9273
19.5%
0 6700
14.1%
2 5722
12.0%
5 4753
10.0%
3 4002
8.4%
4 3757
7.9%
6 3709
 
7.8%
7 3278
 
6.9%
8 3184
 
6.7%
9 3166
 
6.7%
Uppercase Letter
ValueCountFrequency (%)
B 3034
35.0%
C 1887
21.8%
S 1876
21.7%
U 1158
 
13.4%
P 705
 
8.1%
Other Punctuation
ValueCountFrequency (%)
. 20565
87.1%
: 3045
 
12.9%
Space Separator
ValueCountFrequency (%)
26676
100.0%
Close Punctuation
ValueCountFrequency (%)
) 5588
100.0%
Open Punctuation
ValueCountFrequency (%)
( 5588
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 109006
54.2%
Latin 92063
45.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 13279
14.4%
r 9529
10.4%
e 9389
10.2%
s 7634
8.3%
q 7494
8.1%
t 7383
8.0%
p 6797
7.4%
u 6786
7.4%
m 5597
 
6.1%
l 3739
 
4.1%
Other values (10) 14436
15.7%
Common
ValueCountFrequency (%)
26676
24.5%
. 20565
18.9%
1 9273
 
8.5%
0 6700
 
6.1%
2 5722
 
5.2%
) 5588
 
5.1%
( 5588
 
5.1%
5 4753
 
4.4%
3 4002
 
3.7%
4 3757
 
3.4%
Other values (5) 16382
15.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 201069
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
26676
 
13.3%
. 20565
 
10.2%
a 13279
 
6.6%
r 9529
 
4.7%
e 9389
 
4.7%
1 9273
 
4.6%
s 7634
 
3.8%
q 7494
 
3.7%
t 7383
 
3.7%
p 6797
 
3.4%
Other values (25) 83050
41.3%

bedRoom
Real number (ℝ)

Distinct20
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.369086
Minimum1
Maximum36
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size58.1 KiB
2023-09-22T12:11:05.460001image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median3
Q34
95-th percentile6
Maximum36
Range35
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.968032
Coefficient of variation (CV)0.58414419
Kurtosis35.436854
Mean3.369086
Median Absolute Deviation (MAD)1
Skewness4.3057819
Sum12533
Variance3.87315
MonotonicityNot monotonic
2023-09-22T12:11:05.568804image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
3 1507
40.5%
2 951
25.6%
4 670
18.0%
5 213
 
5.7%
1 128
 
3.4%
6 78
 
2.1%
9 41
 
1.1%
8 31
 
0.8%
7 28
 
0.8%
12 28
 
0.8%
Other values (10) 45
 
1.2%
ValueCountFrequency (%)
1 128
 
3.4%
2 951
25.6%
3 1507
40.5%
4 670
18.0%
5 213
 
5.7%
6 78
 
2.1%
7 28
 
0.8%
8 31
 
0.8%
9 41
 
1.1%
10 20
 
0.5%
ValueCountFrequency (%)
36 1
 
< 0.1%
21 1
 
< 0.1%
20 1
 
< 0.1%
19 2
 
0.1%
18 2
 
0.1%
16 12
0.3%
14 1
 
< 0.1%
13 4
 
0.1%
12 28
0.8%
11 1
 
< 0.1%

bathroom
Real number (ℝ)

Distinct20
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4284946
Minimum1
Maximum36
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size58.1 KiB
2023-09-22T12:11:05.714833image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median3
Q34
95-th percentile6
Maximum36
Range35
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.0161341
Coefficient of variation (CV)0.5880523
Kurtosis33.035775
Mean3.4284946
Median Absolute Deviation (MAD)1
Skewness4.0274709
Sum12754
Variance4.0647969
MonotonicityNot monotonic
2023-09-22T12:11:05.830278image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
3 1086
29.2%
2 1061
28.5%
4 830
22.3%
5 295
 
7.9%
1 161
 
4.3%
6 118
 
3.2%
7 41
 
1.1%
9 41
 
1.1%
8 26
 
0.7%
12 22
 
0.6%
Other values (10) 39
 
1.0%
ValueCountFrequency (%)
1 161
 
4.3%
2 1061
28.5%
3 1086
29.2%
4 830
22.3%
5 295
 
7.9%
6 118
 
3.2%
7 41
 
1.1%
8 26
 
0.7%
9 41
 
1.1%
10 9
 
0.2%
ValueCountFrequency (%)
36 1
 
< 0.1%
21 1
 
< 0.1%
20 3
 
0.1%
18 4
 
0.1%
17 3
 
0.1%
16 8
 
0.2%
14 2
 
0.1%
13 4
 
0.1%
12 22
0.6%
11 4
 
0.1%

balcony
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size240.9 KiB
3+
1178 
3
1078 
2
902 
1
378 
0
184 

Length

Max length2
Median length1
Mean length1.3166667
Min length1

Characters and Unicode

Total characters4898
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3+
2nd row1
3rd row3
4th row3
5th row0

Common Values

ValueCountFrequency (%)
3+ 1178
31.7%
3 1078
29.0%
2 902
24.2%
1 378
 
10.2%
0 184
 
4.9%

Length

2023-09-22T12:11:05.969802image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-22T12:11:06.115243image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
3 2256
60.6%
2 902
 
24.2%
1 378
 
10.2%
0 184
 
4.9%

Most occurring characters

ValueCountFrequency (%)
3 2256
46.1%
+ 1178
24.1%
2 902
 
18.4%
1 378
 
7.7%
0 184
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3720
75.9%
Math Symbol 1178
 
24.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 2256
60.6%
2 902
 
24.2%
1 378
 
10.2%
0 184
 
4.9%
Math Symbol
ValueCountFrequency (%)
+ 1178
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4898
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 2256
46.1%
+ 1178
24.1%
2 902
 
18.4%
1 378
 
7.7%
0 184
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4898
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 2256
46.1%
+ 1178
24.1%
2 902
 
18.4%
1 378
 
7.7%
0 184
 
3.8%

floorNum
Real number (ℝ)

Distinct43
Distinct (%)1.2%
Missing20
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean6.7486486
Minimum0
Maximum51
Zeros130
Zeros (%)3.5%
Negative0
Negative (%)0.0%
Memory size58.1 KiB
2023-09-22T12:11:06.285535image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median5
Q310
95-th percentile18
Maximum51
Range51
Interquartile range (IQR)8

Descriptive statistics

Standard deviation6.0063552
Coefficient of variation (CV)0.89000858
Kurtosis4.5362872
Mean6.7486486
Median Absolute Deviation (MAD)3
Skewness1.7029404
Sum24970
Variance36.076303
MonotonicityNot monotonic
2023-09-22T12:11:06.428054image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
3 507
13.6%
2 505
13.6%
1 367
 
9.9%
4 319
 
8.6%
8 196
 
5.3%
6 182
 
4.9%
10 179
 
4.8%
7 178
 
4.8%
5 168
 
4.5%
9 161
 
4.3%
Other values (33) 938
25.2%
ValueCountFrequency (%)
0 130
 
3.5%
1 367
9.9%
2 505
13.6%
3 507
13.6%
4 319
8.6%
5 168
 
4.5%
6 182
 
4.9%
7 178
 
4.8%
8 196
 
5.3%
9 161
 
4.3%
ValueCountFrequency (%)
51 1
 
< 0.1%
45 1
 
< 0.1%
44 1
 
< 0.1%
43 2
0.1%
40 1
 
< 0.1%
39 2
0.1%
38 1
 
< 0.1%
35 2
0.1%
34 2
0.1%
33 4
0.1%

facing
Categorical

HIGH CORRELATION  MISSING 

Distinct8
Distinct (%)0.3%
Missing1061
Missing (%)28.5%
Memory size228.0 KiB
North-East
631 
East
630 
North
394 
West
250 
South
232 
Other values (3)
522 

Length

Max length10
Median length5
Mean length6.8371568
Min length4

Characters and Unicode

Total characters18180
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSouth-West
2nd rowEast
3rd rowNorth
4th rowNorth
5th rowNorth

Common Values

ValueCountFrequency (%)
North-East 631
17.0%
East 630
16.9%
North 394
 
10.6%
West 250
 
6.7%
South 232
 
6.2%
North-West 194
 
5.2%
South-East 174
 
4.7%
South-West 154
 
4.1%
(Missing) 1061
28.5%

Length

2023-09-22T12:11:06.584644image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-22T12:11:06.746868image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
north-east 631
23.7%
east 630
23.7%
north 394
14.8%
west 250
 
9.4%
south 232
 
8.7%
north-west 194
 
7.3%
south-east 174
 
6.5%
south-west 154
 
5.8%

Most occurring characters

ValueCountFrequency (%)
t 3812
21.0%
s 2033
11.2%
o 1779
9.8%
h 1779
9.8%
E 1435
 
7.9%
a 1435
 
7.9%
N 1219
 
6.7%
r 1219
 
6.7%
- 1153
 
6.3%
W 598
 
3.3%
Other values (3) 1718
9.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 13215
72.7%
Uppercase Letter 3812
 
21.0%
Dash Punctuation 1153
 
6.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 3812
28.8%
s 2033
15.4%
o 1779
13.5%
h 1779
13.5%
a 1435
 
10.9%
r 1219
 
9.2%
e 598
 
4.5%
u 560
 
4.2%
Uppercase Letter
ValueCountFrequency (%)
E 1435
37.6%
N 1219
32.0%
W 598
15.7%
S 560
 
14.7%
Dash Punctuation
ValueCountFrequency (%)
- 1153
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 17027
93.7%
Common 1153
 
6.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 3812
22.4%
s 2033
11.9%
o 1779
10.4%
h 1779
10.4%
E 1435
 
8.4%
a 1435
 
8.4%
N 1219
 
7.2%
r 1219
 
7.2%
W 598
 
3.5%
e 598
 
3.5%
Other values (2) 1120
 
6.6%
Common
ValueCountFrequency (%)
- 1153
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 18180
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 3812
21.0%
s 2033
11.2%
o 1779
9.8%
h 1779
9.8%
E 1435
 
7.9%
a 1435
 
7.9%
N 1219
 
6.7%
r 1219
 
6.7%
- 1153
 
6.3%
W 598
 
3.3%
Other values (3) 1718
9.4%

agePossession
Categorical

Distinct6
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size284.7 KiB
Relatively New
1653 
New Property
596 
Moderately Old
572 
Undefined
319 
Old Property
315 

Length

Max length18
Median length14
Mean length13.366398
Min length9

Characters and Unicode

Total characters49723
Distinct characters25
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRelatively New
2nd rowRelatively New
3rd rowRelatively New
4th rowModerately Old
5th rowRelatively New

Common Values

ValueCountFrequency (%)
Relatively New 1653
44.4%
New Property 596
 
16.0%
Moderately Old 572
 
15.4%
Undefined 319
 
8.6%
Old Property 315
 
8.5%
Under Construction 265
 
7.1%

Length

2023-09-22T12:11:06.894825image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-22T12:11:07.037709image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
new 2249
31.6%
relatively 1653
23.2%
property 911
12.8%
old 887
 
12.5%
moderately 572
 
8.0%
undefined 319
 
4.5%
under 265
 
3.7%
construction 265
 
3.7%

Most occurring characters

ValueCountFrequency (%)
e 8513
17.1%
l 4765
 
9.6%
t 3666
 
7.4%
3401
 
6.8%
y 3136
 
6.3%
r 2924
 
5.9%
d 2362
 
4.8%
N 2249
 
4.5%
w 2249
 
4.5%
i 2237
 
4.5%
Other values (15) 14221
28.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 39201
78.8%
Uppercase Letter 7121
 
14.3%
Space Separator 3401
 
6.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 8513
21.7%
l 4765
12.2%
t 3666
9.4%
y 3136
 
8.0%
r 2924
 
7.5%
d 2362
 
6.0%
w 2249
 
5.7%
i 2237
 
5.7%
a 2225
 
5.7%
o 2013
 
5.1%
Other values (7) 5111
13.0%
Uppercase Letter
ValueCountFrequency (%)
N 2249
31.6%
R 1653
23.2%
P 911
12.8%
O 887
 
12.5%
U 584
 
8.2%
M 572
 
8.0%
C 265
 
3.7%
Space Separator
ValueCountFrequency (%)
3401
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 46322
93.2%
Common 3401
 
6.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 8513
18.4%
l 4765
 
10.3%
t 3666
 
7.9%
y 3136
 
6.8%
r 2924
 
6.3%
d 2362
 
5.1%
N 2249
 
4.9%
w 2249
 
4.9%
i 2237
 
4.8%
a 2225
 
4.8%
Other values (14) 11996
25.9%
Common
ValueCountFrequency (%)
3401
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 49723
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 8513
17.1%
l 4765
 
9.6%
t 3666
 
7.4%
3401
 
6.8%
y 3136
 
6.3%
r 2924
 
5.9%
d 2362
 
4.8%
N 2249
 
4.5%
w 2249
 
4.5%
i 2237
 
4.5%
Other values (15) 14221
28.6%

super_built_up_area
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct594
Distinct (%)31.7%
Missing1844
Missing (%)49.6%
Infinite0
Infinite (%)0.0%
Mean1923.7029
Minimum89
Maximum10000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size58.1 KiB
2023-09-22T12:11:07.223848image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum89
5-th percentile748.75
Q11474.25
median1828
Q32215
95-th percentile3185
Maximum10000
Range9911
Interquartile range (IQR)740.75

Descriptive statistics

Standard deviation765.37637
Coefficient of variation (CV)0.39786622
Kurtosis10.28315
Mean1923.7029
Median Absolute Deviation (MAD)372
Skewness1.8266761
Sum3608866.5
Variance585800.99
MonotonicityNot monotonic
2023-09-22T12:11:07.384267image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1650 37
 
1.0%
1950 37
 
1.0%
2000 25
 
0.7%
1578 25
 
0.7%
2150 22
 
0.6%
1640 22
 
0.6%
2408 19
 
0.5%
1900 19
 
0.5%
1930 18
 
0.5%
2812 17
 
0.5%
Other values (584) 1635
44.0%
(Missing) 1844
49.6%
ValueCountFrequency (%)
89 1
< 0.1%
145 1
< 0.1%
161 1
< 0.1%
215 1
< 0.1%
216 1
< 0.1%
325 1
< 0.1%
340 1
< 0.1%
352 1
< 0.1%
380 1
< 0.1%
406 1
< 0.1%
ValueCountFrequency (%)
10000 1
< 0.1%
6926 1
< 0.1%
6000 1
< 0.1%
5800 2
0.1%
5514 1
< 0.1%
5350 2
0.1%
5200 2
0.1%
4890 1
< 0.1%
4857 1
< 0.1%
4848 2
0.1%

built_up_area
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct653
Distinct (%)37.9%
Missing1995
Missing (%)53.6%
Infinite0
Infinite (%)0.0%
Mean2407.8
Minimum2
Maximum737147
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size58.1 KiB
2023-09-22T12:11:07.552872image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile260.2
Q11115
median1660
Q32430
95-th percentile4756
Maximum737147
Range737145
Interquartile range (IQR)1315

Descriptive statistics

Standard deviation17770.858
Coefficient of variation (CV)7.3805375
Kurtosis1697.8762
Mean2407.8
Median Absolute Deviation (MAD)640
Skewness41.045212
Sum4153454.9
Variance3.1580339 × 108
MonotonicityNot monotonic
2023-09-22T12:11:07.763942image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1800 41
 
1.1%
3240 38
 
1.0%
1350 34
 
0.9%
1900 34
 
0.9%
2700 33
 
0.9%
900 32
 
0.9%
1600 26
 
0.7%
1300 24
 
0.6%
2000 24
 
0.6%
1700 23
 
0.6%
Other values (643) 1416
38.1%
(Missing) 1995
53.6%
ValueCountFrequency (%)
2 1
 
< 0.1%
14 1
 
< 0.1%
30 1
 
< 0.1%
45 1
 
< 0.1%
50 3
0.1%
53 1
 
< 0.1%
55 1
 
< 0.1%
56 1
 
< 0.1%
57 1
 
< 0.1%
60 4
0.1%
ValueCountFrequency (%)
737147 1
 
< 0.1%
26000 1
 
< 0.1%
13500 1
 
< 0.1%
11286 1
 
< 0.1%
9500 1
 
< 0.1%
9000 9
0.2%
8775 1
 
< 0.1%
8286 1
 
< 0.1%
8067.8 1
 
< 0.1%
8000 1
 
< 0.1%

carpet_area
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct734
Distinct (%)38.9%
Missing1833
Missing (%)49.3%
Infinite0
Infinite (%)0.0%
Mean2518.1706
Minimum15
Maximum607936
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size58.1 KiB
2023-09-22T12:11:07.969027image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile350
Q1832.48
median1300
Q31787.5
95-th percentile2950
Maximum607936
Range607921
Interquartile range (IQR)955.02

Descriptive statistics

Standard deviation22709.454
Coefficient of variation (CV)9.0182351
Kurtosis609.38997
Mean2518.1706
Median Absolute Deviation (MAD)476
Skewness24.430326
Sum4751787.9
Variance5.1571931 × 108
MonotonicityNot monotonic
2023-09-22T12:11:08.203296image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1400 42
 
1.1%
1800 35
 
0.9%
1600 35
 
0.9%
1200 31
 
0.8%
1500 29
 
0.8%
1350 28
 
0.8%
1650 28
 
0.8%
1300 23
 
0.6%
1000 22
 
0.6%
1450 22
 
0.6%
Other values (724) 1592
42.8%
(Missing) 1833
49.3%
ValueCountFrequency (%)
15 1
 
< 0.1%
33 1
 
< 0.1%
48 1
 
< 0.1%
50 1
 
< 0.1%
59 1
 
< 0.1%
60 1
 
< 0.1%
66 1
 
< 0.1%
72 1
 
< 0.1%
76.44 3
0.1%
77.31 1
 
< 0.1%
ValueCountFrequency (%)
607936 1
< 0.1%
569243 1
< 0.1%
514396 1
< 0.1%
64529 1
< 0.1%
64412 1
< 0.1%
58141 1
< 0.1%
54917 1
< 0.1%
48811 1
< 0.1%
45966 1
< 0.1%
34401 1
< 0.1%

study room
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size239.8 KiB
0
3009 
1
711 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3720
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3009
80.9%
1 711
 
19.1%

Length

2023-09-22T12:11:08.400779image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-22T12:11:08.536159image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
0 3009
80.9%
1 711
 
19.1%

Most occurring characters

ValueCountFrequency (%)
0 3009
80.9%
1 711
 
19.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3720
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3009
80.9%
1 711
 
19.1%

Most occurring scripts

ValueCountFrequency (%)
Common 3720
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3009
80.9%
1 711
 
19.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3720
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3009
80.9%
1 711
 
19.1%

servant room
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size239.8 KiB
0
2383 
1
1337 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3720
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2383
64.1%
1 1337
35.9%

Length

2023-09-22T12:11:08.645549image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-22T12:11:08.786502image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
0 2383
64.1%
1 1337
35.9%

Most occurring characters

ValueCountFrequency (%)
0 2383
64.1%
1 1337
35.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3720
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2383
64.1%
1 1337
35.9%

Most occurring scripts

ValueCountFrequency (%)
Common 3720
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2383
64.1%
1 1337
35.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3720
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2383
64.1%
1 1337
35.9%

store room
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size239.8 KiB
0
3376 
1
344 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3720
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3376
90.8%
1 344
 
9.2%

Length

2023-09-22T12:11:08.902997image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-22T12:11:09.142915image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
0 3376
90.8%
1 344
 
9.2%

Most occurring characters

ValueCountFrequency (%)
0 3376
90.8%
1 344
 
9.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3720
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3376
90.8%
1 344
 
9.2%

Most occurring scripts

ValueCountFrequency (%)
Common 3720
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3376
90.8%
1 344
 
9.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3720
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3376
90.8%
1 344
 
9.2%

pooja room
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size239.8 KiB
0
3055 
1
665 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3720
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3055
82.1%
1 665
 
17.9%

Length

2023-09-22T12:11:09.279006image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-22T12:11:09.467946image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
0 3055
82.1%
1 665
 
17.9%

Most occurring characters

ValueCountFrequency (%)
0 3055
82.1%
1 665
 
17.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3720
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3055
82.1%
1 665
 
17.9%

Most occurring scripts

ValueCountFrequency (%)
Common 3720
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3055
82.1%
1 665
 
17.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3720
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3055
82.1%
1 665
 
17.9%

others
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size239.8 KiB
0
3309 
1
411 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3720
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3309
89.0%
1 411
 
11.0%

Length

2023-09-22T12:11:09.630859image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-22T12:11:09.803300image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
0 3309
89.0%
1 411
 
11.0%

Most occurring characters

ValueCountFrequency (%)
0 3309
89.0%
1 411
 
11.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3720
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3309
89.0%
1 411
 
11.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3720
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3309
89.0%
1 411
 
11.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3720
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3309
89.0%
1 411
 
11.0%

furnishing_type
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size239.8 KiB
0
2444 
2
1070 
1
 
206

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3720
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2444
65.7%
2 1070
28.8%
1 206
 
5.5%

Length

2023-09-22T12:11:09.952813image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-22T12:11:10.147068image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
0 2444
65.7%
2 1070
28.8%
1 206
 
5.5%

Most occurring characters

ValueCountFrequency (%)
0 2444
65.7%
2 1070
28.8%
1 206
 
5.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3720
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2444
65.7%
2 1070
28.8%
1 206
 
5.5%

Most occurring scripts

ValueCountFrequency (%)
Common 3720
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2444
65.7%
2 1070
28.8%
1 206
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3720
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2444
65.7%
2 1070
28.8%
1 206
 
5.5%

luxury_score
Real number (ℝ)

Distinct161
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean70.978495
Minimum0
Maximum174
Zeros476
Zeros (%)12.8%
Negative0
Negative (%)0.0%
Memory size58.1 KiB
2023-09-22T12:11:10.287711image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q131
median58
Q3110
95-th percentile174
Maximum174
Range174
Interquartile range (IQR)79

Descriptive statistics

Standard deviation53.060813
Coefficient of variation (CV)0.74756183
Kurtosis-0.86907689
Mean70.978495
Median Absolute Deviation (MAD)37
Skewness0.47044229
Sum264040
Variance2815.4499
MonotonicityNot monotonic
2023-09-22T12:11:10.482576image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 476
 
12.8%
49 350
 
9.4%
174 195
 
5.2%
44 61
 
1.6%
38 55
 
1.5%
165 55
 
1.5%
72 52
 
1.4%
60 47
 
1.3%
15 45
 
1.2%
42 45
 
1.2%
Other values (151) 2339
62.9%
ValueCountFrequency (%)
0 476
12.8%
5 6
 
0.2%
6 6
 
0.2%
7 42
 
1.1%
8 31
 
0.8%
9 10
 
0.3%
12 7
 
0.2%
13 11
 
0.3%
14 12
 
0.3%
15 45
 
1.2%
ValueCountFrequency (%)
174 195
5.2%
169 1
 
< 0.1%
168 9
 
0.2%
167 21
 
0.6%
166 10
 
0.3%
165 55
 
1.5%
161 3
 
0.1%
160 28
 
0.8%
159 23
 
0.6%
158 34
 
0.9%

nearbyLoc_score
Real number (ℝ)

Distinct75
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.566129
Minimum0
Maximum98
Zeros199
Zeros (%)5.3%
Negative0
Negative (%)0.0%
Memory size58.1 KiB
2023-09-22T12:11:10.647809image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q139
median55
Q364
95-th percentile81
Maximum98
Range98
Interquartile range (IQR)25

Descriptive statistics

Standard deviation21.204264
Coefficient of variation (CV)0.41933729
Kurtosis-5.4776659 × 10-5
Mean50.566129
Median Absolute Deviation (MAD)12
Skewness-0.72661393
Sum188106
Variance449.62079
MonotonicityNot monotonic
2023-09-22T12:11:10.802979image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
61 300
 
8.1%
0 199
 
5.3%
63 185
 
5.0%
50 172
 
4.6%
62 147
 
4.0%
49 131
 
3.5%
55 117
 
3.1%
64 108
 
2.9%
25 93
 
2.5%
58 83
 
2.2%
Other values (65) 2185
58.7%
ValueCountFrequency (%)
0 199
5.3%
8 44
 
1.2%
9 17
 
0.5%
16 70
 
1.9%
17 25
 
0.7%
18 43
 
1.2%
21 3
 
0.1%
23 19
 
0.5%
24 48
 
1.3%
25 93
2.5%
ValueCountFrequency (%)
98 3
 
0.1%
90 16
 
0.4%
89 10
 
0.3%
88 20
 
0.5%
87 13
 
0.3%
85 15
 
0.4%
84 8
 
0.2%
83 22
 
0.6%
82 42
1.1%
81 65
1.7%

Interactions

2023-09-22T12:10:59.583020image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-22T12:10:40.156675image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-22T12:10:42.096603image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-22T12:10:44.035167image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-22T12:10:46.041219image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-22T12:10:48.090786image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-22T12:10:50.078559image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-22T12:10:52.007495image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-22T12:10:53.785280image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-22T12:10:55.721527image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-22T12:10:57.727666image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-22T12:10:59.764294image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-22T12:10:40.459141image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-22T12:10:42.279099image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-22T12:10:44.164248image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-22T12:10:46.207638image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-22T12:10:48.290403image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-22T12:10:50.271386image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-22T12:10:52.158143image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-22T12:10:53.927499image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-22T12:10:55.905164image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-22T12:10:57.943580image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-22T12:10:59.973480image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-22T12:10:40.604344image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-22T12:10:42.408758image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-22T12:10:44.356476image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-22T12:10:46.404458image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-22T12:10:48.451321image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-22T12:10:50.442593image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-22T12:10:52.323806image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-22T12:10:54.088156image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-22T12:10:56.275353image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-22T12:10:58.112858image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-22T12:11:00.170524image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-22T12:10:40.790106image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-22T12:10:42.606487image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-22T12:10:44.508179image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-22T12:10:46.558075image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-22T12:10:48.617733image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-22T12:10:50.648685image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-22T12:10:52.457531image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-22T12:10:54.272024image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-22T12:10:56.472299image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-22T12:10:58.293312image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-22T12:11:00.351468image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-22T12:10:40.922682image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-22T12:10:42.741193image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-22T12:10:44.696249image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-22T12:10:46.741616image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-22T12:10:48.802477image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-22T12:10:50.825687image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-22T12:10:52.624411image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-22T12:10:54.444882image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-22T12:10:56.615161image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-22T12:10:58.465012image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-22T12:11:00.533716image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-22T12:10:41.114400image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-22T12:10:42.972579image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-22T12:10:44.862813image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-22T12:10:46.908220image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-22T12:10:48.991550image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-22T12:10:50.993526image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-22T12:10:52.776741image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-22T12:10:54.654981image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-22T12:10:56.804634image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-22T12:10:58.623461image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-22T12:11:00.656255image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-22T12:10:41.273084image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-22T12:10:43.117822image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-22T12:10:45.027597image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-22T12:10:47.074581image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-22T12:10:49.141081image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-22T12:10:51.176207image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-22T12:10:52.900683image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-22T12:10:54.830022image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-22T12:10:56.957064image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-22T12:10:58.784659image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-22T12:11:00.822040image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-22T12:10:41.403952image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-22T12:10:43.289703image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-22T12:10:45.204806image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-22T12:10:47.283384image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-22T12:10:49.390460image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-22T12:10:51.315314image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-22T12:10:53.054539image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-22T12:10:54.971086image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-22T12:10:57.123358image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-22T12:10:58.932739image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-22T12:11:01.022319image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-22T12:10:41.618277image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-22T12:10:43.492471image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-22T12:10:45.395774image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-22T12:10:47.483811image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-22T12:10:49.589243image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-22T12:10:51.505519image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-22T12:10:53.293330image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-22T12:10:55.205525image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-22T12:10:57.265461image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-22T12:10:59.068988image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-22T12:11:01.226347image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-22T12:10:41.739040image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-22T12:10:43.641322image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-22T12:10:45.558239image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-22T12:10:47.641444image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-22T12:10:49.736967image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-22T12:10:51.638312image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-22T12:10:53.424040image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-22T12:10:55.365813image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-22T12:10:57.421753image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-22T12:10:59.240633image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-22T12:11:01.429122image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-22T12:10:41.929019image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-22T12:10:43.841000image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-22T12:10:45.895974image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-22T12:10:47.907413image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-22T12:10:49.939257image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-22T12:10:51.791557image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-22T12:10:53.601409image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-22T12:10:55.591180image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-22T12:10:57.584205image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-22T12:10:59.409671image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Correlations

2023-09-22T12:11:11.200977image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
price_crprice_per_sqftareabedRoombathroomfloorNumsuper_built_up_areabuilt_up_areacarpet_arealuxury_scorenearbyLoc_scoreproperty_typebalconyfacingagePossessionstudy roomservant roomstore roompooja roomothersfurnishing_type
price_cr1.0000.7430.7420.6800.7200.0030.7730.6340.6110.2170.3010.5370.1370.0210.1020.2450.3690.3010.3330.0350.179
price_per_sqft0.7431.0000.2020.4170.411-0.1250.2870.1530.1340.0550.3130.2030.0310.0000.0580.0310.0380.0000.0370.0310.000
area0.7420.2021.0000.6190.6840.1190.9480.8630.8010.2620.1360.0270.0090.0220.0000.0180.0150.0380.0370.0420.043
bedRoom0.6800.4170.6191.0000.860-0.1010.8000.3970.5660.0570.1590.6170.1140.0380.1170.1640.1410.2040.2710.0700.138
bathroom0.7200.4110.6840.8601.000-0.0010.8190.4870.5950.1830.1930.4400.1620.0350.0790.1620.3110.1740.2660.0710.164
floorNum0.003-0.1250.119-0.101-0.0011.0000.1540.0830.1610.240-0.0580.4870.0800.0000.1270.0770.0850.1120.1030.0330.018
super_built_up_area0.7730.2870.9480.8000.8190.1541.0000.9260.8940.2240.1211.0000.3070.0000.0860.1220.5840.0470.1570.0860.134
built_up_area0.6340.1530.8630.3970.4870.0830.9261.0000.9680.2860.1000.0000.0001.0000.0000.0000.0000.0000.0000.0000.088
carpet_area0.6110.1340.8010.5660.5950.1610.8940.9681.0000.2420.1140.0000.0240.0000.0000.0040.0000.0000.0000.0150.000
luxury_score0.2170.0550.2620.0570.1830.2400.2240.2860.2421.0000.1250.3340.2240.0650.2560.1860.3480.2260.1910.1750.247
nearbyLoc_score0.3010.3130.1360.1590.193-0.0580.1210.1000.1140.1251.0000.2580.1690.0550.2050.1350.2010.1310.1260.0630.136
property_type0.5370.2030.0270.6170.4400.4871.0000.0000.0000.3340.2581.0000.2110.0980.3870.1270.0630.2420.2530.0270.079
balcony0.1370.0310.0090.1140.1620.0800.3070.0000.0240.2240.1690.2111.0000.0220.2710.1850.4420.1440.1950.0800.180
facing0.0210.0000.0220.0380.0350.0000.0001.0000.0000.0650.0550.0980.0221.0000.0920.0000.0330.0360.0250.0000.049
agePossession0.1020.0580.0000.1170.0790.1270.0860.0000.0000.2560.2050.3870.2710.0921.0000.1430.2870.1470.1880.1110.215
study room0.2450.0310.0180.1640.1620.0770.1220.0000.0040.1860.1350.1270.1850.0000.1431.0000.1870.2230.3160.0330.147
servant room0.3690.0380.0150.1410.3110.0850.5840.0000.0000.3480.2010.0630.4420.0330.2870.1871.0000.1590.2530.0000.273
store room0.3010.0000.0380.2040.1740.1120.0470.0000.0000.2260.1310.2420.1440.0360.1470.2230.1591.0000.3020.1070.156
pooja room0.3330.0370.0370.2710.2660.1030.1570.0000.0000.1910.1260.2530.1950.0250.1880.3160.2530.3021.0000.0340.219
others0.0350.0310.0420.0700.0710.0330.0860.0000.0150.1750.0630.0270.0800.0000.1110.0330.0000.1070.0341.0000.057
furnishing_type0.1790.0000.0430.1380.1640.0180.1340.0880.0000.2470.1360.0790.1800.0490.2150.1470.2730.1560.2190.0571.000

Missing values

2023-09-22T12:11:01.734721image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
A simple visualization of nullity by column.
2023-09-22T12:11:02.319724image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-09-22T12:11:02.769191image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

property_typesocietysectorprice_crprice_per_sqftareaareaWithTypebedRoombathroombalconyfloorNumfacingagePossessionsuper_built_up_areabuilt_up_areacarpet_areastudy roomservant roomstore roompooja roomothersfurnishing_typeluxury_scorenearbyLoc_score
0flatexperion the heartsongsector 1083.0018392.01631.0Super Built up area 2779(258.18 sq.m.)Built Up area: 2204.25 sq.ft. (204.78 sq.m.)Carpet area: 1631.07 sq.ft. (151.53 sq.m.)453+2.0South-WestRelatively New2779.02204.251631.0701000114958
1flattulip violetsector 691.378675.01579.0Super Built up area 1578(146.6 sq.m.)Carpet area: 1538 sq.ft. (142.88 sq.m.)33112.0EastRelatively New1578.0NaN1538.000001009561
2flatshree vardhman victoriasector 701.6213953.01161.0Super Built up area 1950(181.16 sq.m.)Carpet area: 1161 sq.ft. (107.86 sq.m.)3339.0NorthRelatively New1950.0NaN1161.000100009636
3flatsare green parc phase 3sector 920.954856.01956.0Super Built up area 1956(181.72 sq.m.)4334.0NorthModerately Old1956.0NaNNaN0000006749
4flatmvn athenssohna road0.255198.0481.0Built Up area: 481 (44.69 sq.m.)2200.0NaNRelatively NewNaN481.00NaN0000003729
5flatdlf the belairesector 5410.0024557.04072.0Super Built up area 4072(378.3 sq.m.)Built Up area: 3000 sq.ft. (278.71 sq.m.)Carpet area: 2800 sq.ft. (260.13 sq.m.)453+17.0NorthModerately Old4072.03000.002800.0001000116757
6flatumang winter hillssector 770.925049.01822.0Super Built up area 1822(169.27 sq.m.)Carpet area: 1400 sq.ft. (130.06 sq.m.)33317.0North-EastRelatively New1822.0NaN1400.000000124964
7houseindependentsector 122.7512343.02228.0Built Up area: 2228 (206.99 sq.m.)6622.0NaNUndefinedNaN2228.00NaN00000000
8flatorchid petalssector 494.2111835.03557.0Super Built up area 3557(330.46 sq.m.)563+14.0South-EastRelatively New3557.0NaNNaN0101003552
9houseindependentsector 110.957308.01300.0Built Up area: 1300 (120.77 sq.m.)5301.0NaNUndefinedNaN1300.00NaN00000000
property_typesocietysectorprice_crprice_per_sqftareaareaWithTypebedRoombathroombalconyfloorNumfacingagePossessionsuper_built_up_areabuilt_up_areacarpet_areastudy roomservant roomstore roompooja roomothersfurnishing_typeluxury_scorenearbyLoc_score
3831houseindependentsector 280.4590000.050.0Built Up area: 50 (4.65 sq.m.)5301.0NaNUndefinedNaN50.0NaN00000000
3832flatsupertech aravillesector 791.359000.01500.0Super Built up area 1530(142.14 sq.m.)Built Up area: 1350 sq.ft. (125.42 sq.m.)Carpet area: 1200 sq.ft. (111.48 sq.m.)2236.0EastNew Property1530.001350.01200.00100012888
3833flatparas dewssector 1061.2011605.01034.0Super Built up area 1665(154.68 sq.m.)Built Up area: 1145 sq.ft. (106.37 sq.m.)Carpet area: 1034 sq.ft. (96.06 sq.m.)33312.0North-WestRelatively New1665.001145.01034.0010000015873
3834flatsignature global solerasector 1070.365980.0602.0Super Built up area 602(55.93 sq.m.)Built Up area: 598 sq.ft. (55.56 sq.m.)Carpet area: 546 sq.ft. (50.73 sq.m.)2217.0North-WestRelatively New602.00598.0546.000000104968
3835flatgodrej habitatsector 31.1010410.01057.0Super Built up area 1056.58(98.16 sq.m.)22214.0NaNUnder Construction1056.58NaNNaN0000006081
3836houseproject mianwali colonysector 121.709444.01800.0Carpet area: 1800 (167.23 sq.m.)1121.0NaNUndefinedNaNNaN1800.00000000061
3837flatats kocoonsector 1091.609169.01745.0Super Built up area 1745(162.12 sq.m.)Built Up area: 1550 sq.ft. (144 sq.m.)33310.0NorthRelatively New1745.001550.0NaN0000008655
3838flatorris aster court premiersector 851.505859.02560.0Super Built up area 2560(237.83 sq.m.)Built Up area: 2017 sq.ft. (187.39 sq.m.)Carpet area: 1800 sq.ft. (167.23 sq.m.)453+6.0WestNew Property2560.002017.01800.000100022853
3839flatss the leafsector 851.096666.01635.0Super Built up area 1640(152.36 sq.m.)Built Up area: 1638 sq.ft. (152.18 sq.m.)Carpet area: 1635 sq.ft. (151.9 sq.m.)2239.0South-WestRelatively New1640.001638.01635.0000100017463
3840flatsignature the roseliasector 950.416194.0662.0Built Up area: 670 (62.25 sq.m.)Carpet area: 569.25 sq.ft. (52.89 sq.m.)22212.0NorthNew PropertyNaN670.0569.251000029040